The broad long-term goal of this project was to develop theories, algorithms, and their portable computer implementations for quantifying object information captured in multidimensional images and to apply them to solve specific biomedical problems. The lack of cost-effective methods with proven precision and accuracy for extracting object information from images remains one of the major impediments in many radiological applications. With this in mind, the applicants proposed: (1) to advance the theory, algorithms, and their efficient computer implementations for detecting and delineating objects in multidimensional, multiparametric images in general, and (2) to apply these methods to the problem of quantifying MS lesions of the brain via MR imagery. The methods proposed in this application are based on a theory relating to definition of objects in images. Since images are by nature fuzzy, the theory considers objects as a set of image elements that "hang-together" fuzzily. A fuzzy topological concept called fuzzy connectedness is introduced that captures the idea of "hanging-togetherness." Although the concept is computationally impractical, key theoretical results lead to practical computer algorithms for detecting fuzzy objects in images. MS is an intensively studied disease of the nervous system. Its detection and quantification via MR images has proved critical to the monitoring of this disease and of its response to therapy. However, a practical solution to this problem is still not available. Supported by strong preliminary results, this application proposes practical methods based on fuzzy connectedness for the detection and quantification of MS lesions. The overall hypothesis underlying this research is that the methods resulting from this investigation will be practical, cost-effective, and reliable for quantifying MS lesions with a precision and accuracy that is acceptable for conducting clinical trials.